This project demonstrates how to build and visualize a Decision Tree Classifier using Scikit-learn. The model is trained on the Iris dataset to classify flowers into three species based on features such as petal and sepal length and width.
- Python
- Scikit-learn
- Pandas
- Matplotlib & Seaborn
- Jupyter Notebook
- Pairplot to understand feature distribution
- Decision Tree plot to visualize model logic
- Classifier trained using
entropycriterion and depth-limited to 3 - Accuracy score and classification report generated
- High interpretability through tree visualization
The model performs well on the test set and provides interpretable visual splits of decision logic.
decision_tree_implementation.ipynb: Main notebook with code, visualization, and analysis
- Clone the repository
- Open the notebook in Jupyter or VSCode
- Run all cells from top to bottom
The model achieves high accuracy on the Iris dataset and provides clear visualization of the decision-making process.
Feel free to explore and modify the notebook to improve or apply it on different datasets.
This project is open-source and available for educational purposes.
Decision Trees are a simple yet powerful tool in machine learning. This project shows how easy it is to implement and understand them with real-world datasets.
______Author:- Bhooma Anand